Article
Correction of Interferometric and Vegetation Biases in
the SRTMGL1 Spaceborne DEM with Hydrological
Conditioning towards Improved Hydrodynamics
Modeling in the Amazon Basin
Sebastien Pinel 1,2, *, Marie-Paule Bonnet 2,3,† , Joecila Santos Da Silva 1,2,† , Daniel Moreira 4,† ,
Stephane Calmant 2,4,5,† , Fredéric Satgé 2,6,† and Fredérique Seyler 2,7,†
Received: 19 August 2015; Accepted: 19 November 2015; Published: 2 December 2015
Academic Editors: Guy J-P. Schumann, Magaly Koch and Prasad S. Thenkabail
1
2
3
4
5
6
7
*
†
RHASA/ State of Amazonas University (UEA), Av. Darcy Vargas, 1200, Parque 10, 69050-020 Manaus, Brazil;
[email protected] (J.S.D.S.)
Mixed Laboratory International, Observatory for Environmental Change (LMI-OCE),
Institute of Research for Development (IRD)/University of Brasilia (UnB), Campus Darcy Ribeiro,
70910-900 Brasília, Brazil; [email protected] (M.-P.B.); [email protected] (S.C.);
[email protected] (F.S.); [email protected] (F.S.)
UMR 5563 GET/Institute of Research for Development (IRD), 14 avenue Edouard Belin,
31400 Toulouse, France
Geological Survey of Brazil (CPRM), Av. Pasteur, 404, Urca, 22290-040 Rio de Janeiro, Brazil;
[email protected]
UMR 5566 LEGOS/Institute of Research for Development (IRD), 14 avenue Edouard Belin,
31400 Toulouse, France
Institute of Geosciences (LAGEQ), University of Brasília (UnB), Campus Darcy Ribeiro,
70910-900 Brasília, Brazil
UMR 228 ESPACE-DEV/Institute of Research for Development (IRD), 500 rue JF Breton,
34093 Montpellier, France
Correspondence: [email protected]; Tel.: +55-92-981-893-332
These authors contributed equally to this work.
Abstract: In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1) remains
the best topographic information for hydrological and hydrodynamic modeling purposes. However,
its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous
efforts to remove the vegetation signal either did not account for its spatial variability or relied on
a single assumed percentage of penetration of the SRTM signal. Here, we propose a systematic
approach over an Amazonian floodplain to remove the vegetation signal, addressing its heterogeneity
by combining estimates of vegetation height and a land cover map. We improve this approach by
interpolating the first results with drainage network, field and altimetry data to obtain a hydrological
conditioned DEM. The averaged interferometric and vegetation biases over the forest zone were
found to be ´2.0 m and 7.4 m, respectively. Comparing the original and corrected DEM, vertical
validation against Ground Control Points shows a RMSE reduction of 64%. Flood extent accuracy,
controlled against Landsat and JERS-1 images, stresses improvements in low and high water periods
(+24% and +18%, respectively). This study also highlights that a ground truth drainage network,
as a unique input during the interpolation, achieves reasonable results in terms of flood extent and
hydrological characteristics.
Keywords: DEM; SRTMGL1; Amazon; vegetation; altimetry; floodplain; remote sensing;
lake; bathymetry
Remote Sens. 2015, 7, 16108–16130; doi:10.3390/rs71215822
www.mdpi.com/journal/remotesensing
Remote Sens. 2015, 7, 16108–16130
1. Introduction
Wetlands are widely recognized as vital components of watersheds that provide various
hydrological and ecological functions and valuable services for the society, such as water storage,
groundwater recharge, water quality improvement, carbon storage, and biodiversity conservation [1,2].
In the Amazon basin, wetlands cover 800,000 km2 , or 14% of the lowland basin [3]. The flooded
area nearly doubles between the low and high water periods, at Óbidos (1.93˝ S, 55.5˝ W) the most
downstream gauge, in response to the pronounced discharge seasonality of the Amazon River.
Water storage clearly contributes to the flood wave attenuation and delay in its propagation [4,5].
In comparing two regional approaches for channel hydrodynamic modeling, including or omitting
floodplain storage, these authors found a difference in travel time of approximately 1 month at the
scale of the Solimões and Amazon basin. When flood waters travel across the floodplain, a significant
part of the main channel sediment load is trapped [6–8]. Floodplains also significantly alter the fate of
carbon and nutrients in the basin [9,10]. They are considered as hot spots of biodiversity [11], these
characteristics being mainly interpreted in the framework of the flood pulse concept [12].
Floodplains mainly receive regional water through channelized and diffusive overbank flow from
the main stem. Water in the floodplains also comes from local runoff, groundwater seepage and direct
rainfall, whose contributions vary seasonally and inter-annually [13–15]. The seasonal contribution
and the mixture of these different water sources could be a key driving factor of biochemical and
ecological processes in floodplains [16,17]. Because of their hydrological connectivity, these systems
are threatened by both local and distant anthropic pressures, particularly through regional and local
deforestation and dam construction [18]. Thus far, the possible impacts of climatic trends and anthropic
pressure on floodplain hydrology and ecology have not clearly been stated. Gloor et al. [19] recently
showed that the Amazon basin has presented wetter climatic conditions since 1990.
Considering the strong interactions between water circulation, biogeochemical and ecological
processes, it is important to quantify the river-floodplain fluxes exchanged and the water circulation
patterns within the floodplains. Hydrodynamics models, such as LISFLOOD-FP, originally developed
by Bates and De Roo [20] and progressively improved [21–23], are attractive in this context. This model
(or an adapted version) has been applied in the Amazon, from regional (thousands of km) [5,24] to
medium (hundreds of km) spatial scales [25–27]. However, relatively high quality topographic data are
obviously required to produce consistent results. Indeed, hydrodynamics features in floodplains are in
part controlled by small topographic features [28,29], such as narrow channels that drive floodplain
connectivity at low and medium water levels [27].
To date, the best topographic dataset readily available for the Amazon is from the Shuttle Radar
Topographic Mission (SRTM), described in Farr et al. [30]. Nevertheless, the SRTM includes various
types of errors, the dominant sources of which are summarized in Rodriguez et al. [31]. These errors,
gathered hereafter under the term of interferometric bias, make the data inappropriate for use as they
stand in hydrodynamics models. In moderate topography and low vegetation regions, the residual
motion error of the interferometric baseline mainly contributes to the interferometric bias in a significant
source of absolute height errors. The baseline rolls error results in a residual long-wavelength ˘2 m
error with peak values of ˘10 m [31]. In addition, as in most remote-sensed digital elevation models
(DEM), land cover impacts the vertical accuracy of the SRTM: the denser the vegetation, the more
important the elevation offset is. This effect is due to the inability of C-band radar (5.6 cm) to fully
reach the bare earth in the presence of a forest canopy. The capacity of SRTM to detect bare earth is
a function of vegetation characteristics such as tree height, density, branching angle, soil moisture,
and wood moisture [32,33]. In the Amazon lowland basin, Carabajal and Hardling [34] estimated the
height error to be 22.4 m and the SRTM elevation to be 40% of the distance from the canopy top to the
ground. The reported error value in height estimation may prevent accurate estimation of the water
exchange between the main stream and its floodplains. It is higher than the flood wave amplitude tide
in most of water level gauges along the Solimões/Amazon River (approximately 12 m in Manacapuru
(3.317˝ S, 60.583˝ W), for example).
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Interferometric bias might be estimated from ground control points (GCPs), as explained in
Rodriguez et al. [31]. However, no systematic method has been developed to remove the positive
vegetation-related offset. At the regional scale, the simplest method consists of removing a uniform
vegetation bias independently of vegetation type. For example, in the view of hydrological modeling at
the Amazonian Basin scale, Coe et al. [35] used a value of 23 m, whereas Paiva et al. [36] assumed 17 m
in forested areas. Such a methodology, however, assumes a uniform spatial error, which is unlikely
and can lead to inconsistent elevations in floodplain regions where spatial variations in vegetation
heights occur naturally [37]. This procedure could result in certain areas of the SRTM being over or
under-elevated, as shown in Paiva et al. [36]. An alternative approach consists of using a vegetation
height map and subtracting a percentage of the vegetation height from the SRTM. From this perspective,
Wilson et al. [27] made vegetation height field measurements in different representative land cover
classes and, using the wetland cover class proposed by Hess et al. [38], built a vegetation map of
their study area consisting of a 285 km-long floodplain segment. Comparing in situ measurements
made at the edge of deforested areas and SRTM elevations along the same ground profile, they
found that the percentage of penetration of the radar signal within the canopy was 50%. Combining
these two sources of information allowed them to produce a corrected DEM. Based on vegetation
height field data, however, this method was difficult to extrapolate to other areas or larger scales.
Going further, and taking advantage of a global remote-sensed vegetation height map produced by
Simard et al. [39], Baugh et al. [25] proposed a sensitivity analysis to assess the influence of the fixed
value to remove the vegetation bias from SRTM data for hydrodynamic modeling. They created a
set of distributed vegetation offsets by applying a fixed percentage of radar signal penetration to the
Simard et al. [39] vegetation height map. Then, they generated a set of DEMs by subtracting these sets
of distributed offset from the SRTM elevation; the best DEM was selected as the one leading to the most
consistent results when comparing the LISFLOOD-FP hydrodynamic model results with independent
hydrometric data. Their study suggested that subtracting 50%–60% of the Simard et al. [39] vegetation
height from the SRTM was most appropriate for the vegetation encountered in the Amazon floodplains.
One weak point of this method is the target criterion, which requires basing the DEM assessment upon
a hydrodynamic modeling application. As models do not represent reality but only approach it, we
should be careful in using models to validate forcing components of the models themselves [40].
In this study, we propose a correction methodology for the latest release of the SRTM, the
SRTM Global 1 arc-second (SRTMGL1) product, with the aim of making this product available
for the hydrological and hydrodynamic modeling of a floodplain located in the Amazon basin.
The correction methodology considers interferometric and vegetation offset corrections and aims
to keep the DEM coherent with hydrologic knowledge acquired during field campaigns, such as
canals of communication between the Solimões and the lake. The elevation dataset merges the
corrected SRTMGL1 elevations and in situ bathymetric data and is interpolated by the ANUDEM v5.3
algorithm [41,42], which is forced by a ground truth drainage network. The methodology remains
relatively simple, and independent of the model. We compare the improvement of the DEMs generated
by each methodology step in terms of vertical accuracy, drainage network quality and flood extent
against GCPs derived from altimetry sensors (e.g., Envisat, ICESat) and inundation maps deduced
from free available remote-sensed product imagery. For each DEM, we also assess the consequences
for the morphological and hydrological characteristics of the floodplain local catchment. This analysis
permits investigation of the usefulness of each data source on the DEM performance, evaluating the
possibility of using the methodology on a larger scale.
2. Method
2.1. Study Site
The Janauacá Floodplain is located between 3.20˝ S–3.25˝ S and between 60.23˝ W–60.13˝ W, along
the right margin of the Solimões River, approximately 40 km upstream from its junction with the Rio
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Negro (Amazon state, Brazil) (Figure 1). It consists of one lake connected with the Solimões River.
According to the classification proposed by Sioli [43], the Solimões River is a white water river, rich
in suspended sediments and nutrients. In contrast, small streams (igarapés) draining the south of
the watershed present properties closer to black waters, which are rich in dissolved organic matter.
According to the rain gauge data in Manacapuru (3.317˝ S, 60.583˝ W), at approximately 40 km from
the study, the mean annual rainfall is 1976 mm/year for the period lasting from 2006 to 2009 [44].
The river water level mean annual fluctuation reaches 12.2 m at the Manacapuru water level gauge
(3.31˝ S, 60.26˝ W), when considering the 2006–2011 period. The Solimões River presents a mono-modal
hydrograph with the water level rising from mid-November until mid-June, when the recession
phase begins.
Figure 1. Study site with the locations of available river stage data and the virtual stations with
background SRTM Global 1 arc-second (SRTMGL1).
2.2. Data Used in the Study
2.2.1. Remote Sensing Data
SRTMGL1 and SWBD
The global 1 arc-second SRTM V3.0 dataset (SRTMGL1) is a joint product of the National
Geospatial-intelligence Agency (NGA) and the National Aeronautics and Space Administration
(NASA). Data were collected during 11 days in February 2000 using dual Spaceborne Imaging Radar
(SIR-C) and dual X-band Synthetic Aperture Radar (X-SAR). From these data, a near global DEM
was generated. By the beginning of January 2015, the SRTMGL1 had been freely released for South
America. Data can be downloaded at NASA’s Earth Observing System Data and Information System
(EOSDIS) website [45]. They are referenced to the World Geodetic System 84 (WGS84) ellipsoid and
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Earth Gravitational Model 1996 (EGM96) geoid. The EGM96 geoidal undulations were replaced by
the EGM08 ones [46], using programs provided by NGA [47] for both geoid models. SRTM data
products have been validated on continental scales: the absolute and relative vertical accuracies over
South America are 6.2 m and 5.5 m, respectively [31]. Rudorff et al. [26] found a local negative bias of
´4.4 m in an Amazonian floodplain located downstream from the Janauacá floodplain. Satgé et al. [48]
found a negative bias of 7.2 m for the Andean Plateau region. In addition to the bias introduced by
interferometric errors, because of the limitation of C-band radar in reaching the bare earth, SRTM data
present an elevation ranging above the bare earth and below the maximum canopy height [33,34].
In the lowland Amazon basin, the vertical height accuracy was estimated to be 22.4 m [34].
Because water surfaces have low radar backscatter, water bodies and coastlines are generally not
well defined. As a by-product of the water body editing, a water mask was generated: the SRTM
Water Body Data (SWBD). It presents the same coverage as SRTMGL1 and is available in ESRIr
Shapefile format [49] from NASA’s EOSDIS. According to Lehner et al. [50], this product presents some
inconsistencies because in some cases, water body depiction required ancillary data sources, such as
Landsat 5 data that were collected much earlier than the shuttle mission.
Wetlands Map
Hess et al. [38] produced a dual–season map of wetland inundation and vegetation for the central
Amazon basin, denoted by WM hereafter. It is available at NASA’s EOSDIS website [45]. In this
product, wetland areas were defined as inland areas that are periodically inundated or permanently
waterlogged, including lakes, rivers, estuaries, and freshwater marshes. The product is based on
mosaicked L-band Synthetic Aperture Radar (SAR) imagery acquired by the Japanese Earth Resources
Satellite-1 (JERS-1) during two periods. In the study area, images were acquired on 10 October 1995
and 27 May 1996 (Hess, personal communication) for the low water (LW) and high water (HW) seasons,
respectively. In addition to water and non-wetland classes, the classification provides 4 classes of
vegetation (herb, woodland, shrub land and forest). The dual season approach allowed splitting these
classes according to their inundated status (permanently inundated, seasonally inundated or never
inundated). To date, with a resolution of 3 arc-second, it is the only freely available product for the
region of the study. WM was validated using high-resolution, geocoded digital videography collected
during aerial surveys. According to Hess et al. [38], the classification accuracy varies from 94% for the
flooded class and 76% for the non-flooded class at the HW level and 84% for the flooded and 89% for
the non-flooded class at the LW level. Flood maps during the LW and HW seasons (denoted LWFM and
HWFM, respectively) deduced from the WM are commonly used to assess the ability of hydrological
and hydraulic models to reproduce the inundation in the central Amazon basin [4,26,27,35,40].
Radar Altimetry Data
Satellite altimetry for continental waters really began in the 2000s, following the work involving
the Geosat satellite [51] and the Topex/Poseidon (T/P) mission [52]. Many studies have now shown
the great ability of radar altimetry missions for retrieving river stages [53–57]. In this study, we
used data from three different radar altimeters, namely ENVISAT/RA-2, T/P and ICESat/GLAS,
available through the CTOH website (Centre de Topographie des Océans et de l'Hydrosphère) [58]. The
ENVISAT/RA-2 altimeter launched by the European Space Agency (ESA) collected data from 2002
to 2012. ENVISAT has a 35-day repeat period; its ground-track presents an inter-track distance of
approximately 80 km at the equator. Comparison at crossovers and with in situ gauges showed that the
quality of the series can be highly variable, with results ranging from 12 cm to several meters [54,56,59].
The T/P altimeter launched by NASA and the CNES (Centre National d’Etudes Spatiales) collected data
from 1992–2004. T/P has a 10-day repeat cycle. Its ground-track presents an inter-track distance of 315
km at the equator. Birkett et al. [52] showed accuracies ranging from 10 cm to several meters (mean
RMSE 1.1 m).
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NASA acquired elevation data from 2003 to 2009 using the GLAS altimeter on the ICESat
satellite. The ground-track repeat cycle is 183 days, yielding 15 km track spacing at the equator.
The ICESat/GLAS mission was initially launched to monitor icecaps. However, many studies have
addressed its capability of monitoring other land covers. ICESat/GLAS instrument demonstrates
height accuracies of 3 cm over water bodies [60–62]. Due to its high accuracy, ICESat/GLAS data can
be used as GCPs [48,60,61]. In this study, we used the most recent release of the GLA14 data (v34),
specific for land-surface elevation. Data are referenced to the TOPEX/Poseidon ellipsoid and can
be downloaded from NASA’s EOSDIS website [45]. We filtered the data by selecting data acquired
at LW, between October and January, outside the SWBD boundaries and inside the herb class of the
wetlands map.
Forest Canopy Height Map
Simard et al. [39] released a global map of Forest Canopy Height (FCHM, hereafter) at 1 km spatial
resolution. This map is based on data from the Moderate Resolution Imaging Spectroradiometer
(MODIS) on NASA’s Terra and Aqua satellites and from the ICESat/GLAS. The Simard et al. [39]
FCHM was created by regressing ICESat RH100 (Relative Height) canopy height measurements
with global grids of annual mean precipitation, precipitation seasonality, annual mean temperature,
temperature seasonality, elevation, and percentage tree cover. It is distributed as an 890 MB GeoTIFF
file at the website [63]. This product by Simard et al. [39] presents an accuracy of 6.1 m compared with
measurements at 66 Fluxnet sites.
Landsat Products
USGS and NASA have managed the Landsat missions since 1972. Recently, the Landsat
archive has been made freely available [64,65]. Landsat 5 Thematic Mapper (TM) and Landsat 7
Enhanced Thematic Mapper Plus (ETM+) imagery has moderate spatial resolution (30 m) and provides
multispectral images (7 or 8 bands) with a short revisit interval (16 days). We chose images with low
cloud contamination that matched the dates of the study.
2.2.2. In Situ Data
Water Level Data
In addition to the water level gauge at Manacapuru, available at the website of the Brazilian
government water agency ANA [66], two gauges were installed in the floodplain at “RL1” at Tilhero
place (3.424˝ S, 60.264˝ W) and “RL2” in the channel (3.368˝ S, 60.193˝ W) connecting the lake to the
Solimões River (Figure 1). A series of daily values is available beginning 1 September 2006. Satellite
radar altimetry provided 5 additional virtual stations from the T/P and ENVISAT missions. The
manual method described in [53,59] was used to build the virtual gauges, which can be defined as any
intersection between a water body surface and radar altimetric ground tracks. Three virtual stations
were gained from the ENVISAT mission (Figure 1): SV2 (3.493˝ S, 60.253˝ W) in the upstream zone,
SV1 (3.392˝ S, 60.239˝ W) in the downstream zone and SVR (3.325˝ S, 60.220˝ W) in the Solimões. Two
virtual stations were created from the T/P passes crossing the Solimões River in the vicinity of the
Manacapuru gauge: SV_076 (3.863˝ S, 61.685˝ W), 147 km upstream of the gauge, and SV_063 (3.172˝ S,
59.947˝ W), 84 km downstream of the gauge.
The Manacapuru water level gauge has been leveled against EGM08 by differential GPS. All the
other water level gauges were leveled with the help of satellite radar altimetry level time series data,
according to the method described in Santos da Silva et al. [56], and validated by high-precision
bi-frequencies Global Positioning System (GPS) stations. Finally, level time series were reported using
the EGM08 geoid. According to the available altimetry data, the slope of the water surface along the
reach between Manacapuru and the virtual station SVR shows a mean of 2 cm/km over the years
2006–2012.
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Gauge element displacements or errors in reading or reporting data are always possible.
The comparison between altimetry data and in situ records made it possible to correct or complete
the in situ measurements, improving confidence in the in situ water level series [67]. In particular, as
shown in Figure 2, the water level time series at the Manacapuru gauge distributed by ANA present
noisy variations during the years 1994 and 1995. To estimate the water level in the lake at the date
of acquisition of the JERS-1 images (RL1 and RL2 records are available from only 2006), we needed
confident water levels from Manacapuru at the same dates. From the T/P series SV_076 and SV_063
T/P, we estimated the water level on October 10, 1995 to be 12.20 m, against the 10.42 m given by the
Manacapuru gauge series. The same reasoning for the HW level gave a difference of 0.2 m between
the altimetry estimation and the Manacapuru gauge level. As the T/P altimetry data have a vertical
accuracy of approximately 0.5 m [59], we did not modify the recorder level provided by the ANA
gauge for the HW level. Then, building a linear regression between the Manacapuru gauge and RL1
over the LW and HW periods, we estimated the water level in the floodplain at the RL1 gauge to be
13.5 m on 10 October 1995 and 22.1 m on 27 May 1996, respectively.
Figure 2. Temporal series of Manacapuru gauge, T/P_076 SV and T/P_063 SV.
Bathymetric in Situ Data
Most of the data were acquired during a field trip organized during June 2012, when the water
level in the floodplain rose exceptionally to a high absolute level of 24.3 m recorded at the RL1 gauge.
The in situ bathymetric data were acquired using an Acoustic Doppler Profiler Current (Teledyne RD
Instruments, ADCP 1200 Hz) linked to a GPS. Everywhere else, we used an echo sounder linked to
a GPS station. ADCP and echo sounder data were checked against manual measurements using a
ruler and showed overall good agreement. Other in situ bathymetric data were acquired in May 2008
and in August 2006. Errors in the bathymetric grid are expected in both depth and position accuracy.
According to the constructor, ADCP has a vertical precision of 1 cm in 99% of the measurements, far
below the errors induced by field conditions (instruments onboard). In this study, we estimated that
the waves would generate errors on the order of 0.2 m. Position error is reported to be less than 15 m
for 95% of the measurements by both instrument constructors, which is less than the grid mesh we
aimed to build (30 m). Examining survey data, we excluded all data points that showed no consistency
with the rest.
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2.3. Method
Prior to analysis, all gridded products except Landsat images were resampled to the SRTMGL1 1
arc-second resolution. All data were projected into the WGS84 Universal Transverse Mercator (UTM)
zone 20S. We gathered the in situ bathymetric data and ICESat points into a global dataset of 201,935
GCPs. We split the latter into two independent sets, 90 GCP and 10 GCP, whose respective sizes were
90% and 10% of the size of the global GCP dataset.
The methodological steps are presented in Figure 3. First, focusing on a window including a large
area around the floodplain and the river, the process begins with the correction of the interferometric
bias, followed by correction of the positive offsets related to land cover and vegetation height to
produce an adjusted DEM (denoted DEM_ADJ hereafter). Then, DEM_ADJ is merged with the 90 GCP
dataset and interpolated using the ANUDEM algorithm constrained by a drainage network to produce
the corrected DEM (denoted DEM_COR hereafter). The drainage network used to force the ANUDEM
algorithm was automatically extracted from SRTMGL1 and corrected by visual inspection using
Landsat images, denoted GTSN hereafter. Several products were generated: DEM_COR, representing
the whole of the inputs; DEM_COR_1, generated from the GTSN and DEM_ADJ (without vegetation
correction); DEM_COR_2, generated without the river network; and DEM_COR_3, generated without
the 90 GCP dataset. To evaluate the weight of each method step and each input during the interpolation
process, these generated products, DEM1 and SRTMGL1, were validated vertically (against the 10
GCPs dataset), horizontally (against maps of flood extent), and hydrologically (against watershed and
river network assessments).
Figure 3. Flow chart of the generation of the different digital elevation models (DEMs) evaluated,
accompanied by values of ANUDEM parameters.
2.3.1. Interferometric Bias and Vegetation Bias Correction
Interferometric Bias Correction
We followed a similar method to Rudorff et al. [26], which consisted of creating two elevation
subsets: one built with bathymetric- and ICESat-data that overlap bare soil areas, and the other one
built using the corresponding SRTM elevations. Bare soil areas were deduced from the WM. The
Mann-Whitney U test was used to exclude the hypothesis of equal means between real elevations
and SRTMGL1 elevations. The bias was computed as the mean difference between the two elevation
subsets mentioned above and uniformly reported over the entire study site domain. The resulting
intermediary adjusted DEM is denoted DEM1 hereafter.
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Vegetation Bias Correction
The bias introduced by the vegetation is a function of vegetation characteristics and may vary
depending on vegetation height and cover density [32,33]. In the potentially flooded region, vegetation
is spread over several types that clearly present differences in height and cover density [27]. In the
non-flooded uplands, the forest is partially replaced by pasture and culture areas and secondary
forest (capoeira). Thus, applying a uniform vegetation elevation correction to each region can lead to
erroneous elevations.
WM classes were used to partition the study area into several regions. In each region, an elevation
correction was computed according to: 1. the type of vegetation and 2. the flooded status, from
elevation. Using the SWBD, the water class issued from WM was split in two: water in the Solimões
(ID61) and water outside the Solimões (ID62). All other classes are strictly derived from WM. As
a whole, 16 regions were identified and summarized in Table 1. All regions except the terra firma
and one forest class (ID41) may be flooded. Consequently, the pixel elevations of these regions must
remain lower than or equal to the HWFM water level (22.1 m as mentioned above). Alternatively,
non-flooded/flooded regions must exhibit pixel elevations ranging between the LWWM and HWWM
water levels. However, a less restrictive criterion was applied to the elevations of permanently flooded
regions: they must be lower than the HWWM water level as the natural criterion for these zones (to be
lower than the LWWM water level) was too restraining.
Table 1. Characteristics of the selected regions and proposed elevation offset.
Classes
ID
11
12
herbs
13
14
21
22
shrub
23
24
wdlands 31
41
42
forest
43
51
terra
511
firma
512
61
water
62
LW
State
HW
State
water
herbs
herbs
flherbs
nfshr
nfshr
flshr
mixed
flwd
nffor
nffor
flfor
nffor
nffor
nffor
water
water
flherbs
water
flherbs
flherbs
water
flshr
water
mixed
flwd
nffor
flfor
flfor
nffor
nffor
nffor
water
water
Elevation
Constraints
Area
%
Mean
DEM1
Elevation
(m)
Mean
Tree
Height
(m)
ď13.5
ě13.5 and ď22.1
ě13.5 and ď22.1
ď22.1
ě13.5 and ď22.1
ě13.5 and ď22.1
ď22.1
ď22.1
ď22.1
ě22.1
ě13.5 and ď22.1
ď22.1
ě22.1
ě22.1
ě22.1
ď13.5
ď13.5
0.1
2.4
1.1
3.8
1
1.7
0.2
0.3
2.6
4.2
7.9
3.6
60.4
28.1
32.2
7.0
3.1
12.8
12.6
16.0
17.8
14.1
21.5
14.1
17.9
20.4
27.5
27.5
25.6
32.8
24.7
39.4
8.8
11.4
10.1
5.6
10.4
9.6
6.3
15.5
13.6
14.9
13.6
18.4
18.2
18.6
22.4
19.9
24.5
1.4
6.7
Mean
Vegetation
Excluded DEM_ADJ
Offset
%
Elevations
(m)
(m)
0
0
0
0
0
0
0
0
0
0
9.9
9.9
67
65
45
40
3
45
8
33
33
33
53
55
12.8
12.6
16.0
17.8
14.1
21.5
14.1
17.9
20.4
27.5
17.9
16.0
0
14.7
0
0
25
26
0
100
24.7
24.7
8.8
-
Observations: wdlands = woodlands; flherbs = flooded herbs; nfshr = non flooded shrub; fdshr = flooded
shrubs; flwd = flooded woodlands; nffor = non flooded forest; flfor = flooded forest.
The adjusted DEM, DEM_ADJ, was obtained by subtracting an elevation offset from the DEM1
pixel within each region and by turning into NODATA all pixels that do not fulfill the elevation
constraints after the offset subtraction.
The elevation vegetation offsets adopted for each considered region are presented in Table 1. The
elevation offset for open water is set to 0. The water out of the Solimões (ID62 class) was turned into
NODATA values, whereas the Solimões (ID61) was kept unchanged. Similarly, the elevation offset
for the herb and shrub regions was set to 0 (defined as small trees with heights lower than 5 m, and
a relatively small cover density [38]). The woodlands vegetation type (ID31) presents an open tree
canopy with approximately 60% coverage [38]. In this condition, the elevation offset due to this type of
vegetation is likely to vary from one pixel to another. As this class represents only a small percentage
of the study area, we chose to maintain a 0 offset.
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The elevation offset to be applied to forested regions was computed from the DEM1 elevation
distribution of the pixels overlapped by the respective classes to reduce the number of pixels that did
not fulfill the elevation constraints after correction. The elevation distributions of the DEM1 pixels
overlapped by the never flooded forest (ID41), intermittently flooded forest (ID42) and always flooded
forest (ID43) regions are shown in Figure 4a–c, respectively. The pixel elevation in the ID41 region must
remain higher than 22.1 m (HWFM water level). The elevation distribution of the DEM1 overlapped
pixels is mono-modal (Figure 4a), with a median value of 26.7 m and with 33% of the pixels presenting
an elevation lower than 22.1 m. As it was difficult to propose a vegetation offset that made sense
while minimizing the quantity of pixels that would have been turned into NODATA after correction,
the vegetation offset was kept undefined, and we turned only pixels less than 22.1 m into NODATA.
The elevation distributions of the pixels overlapped by the ID42 and ID43 regions are mono-modal
(Figure 4b,c), with median values of 27.7 m and 25.6 m, respectively. These regions represent 7.9% and
3.6% of the study zone window, respectively. For the ID42 class, we imposed a requirement that the
median of the corrected elevation distribution be at the middle of the window of acceptable elevations
(13.5 m, 22.1 m) for the ID42 region, that is, 17.8 m. Finally, the vegetation offset obtained for this
region was 9.9 m. The ID43 area presents the same vegetation and histogram characteristics, and
thus the same offset was used for both ID42 and ID43. A value of 9.9 m corresponds to 53% and 52%
of the mean vegetation height deduced from the Simard et al. [39] product over the areas ID42 and
ID43, respectively.
Figure 4. Histograms of DEM1 elevation (a) DEM1 elevation of non-flooded forest class (ID41);
(b) DEM1 elevation of intermittently flooded forest (ID42); (c) DEM1 elevation of always flooded forest
(ID43); (d) DEM1 elevation of terra firma class (ID51).
The elevation distribution of the pixels overlapped by the ID51 region is bi-modal (Figure 4d).
The region was split into two mono modal sub-zones: pixels whose DEM1 elevation was less than
30.0 m, denoted ID511, and the rest, denoted ID-512. A comparison of the spatial repartition of these
pixels with a Landsat image stressed that ID511 was linked to deforested areas surrounding water
bodies or linked to roads and that ID512 corresponded to forested areas. A subtraction of 14.70 m was
used in the ID512 region, assuming the SRTM C-band penetration of 60% proposed by Carabajal and
Harding [34]. However, the elevation range of the pixels located in ID511 was globally smaller and fit
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with the imposed elevation range. Consequently, the elevations of these pixels were kept unchanged
in this zone.
The correction for the influence of vegetation led to turning to NODATA classifications for 31% of
the whole study zone. However, the removal process was slightly counterbalanced by the introduction
of 181,741 points from the 90 GCP dataset during the interpolation process that represents 2.2% of the
wetland zone.
2.3.2. DEM Elevation Interpolation
The second step in the DEM correction procedure consisted of the interpolation of DEM_ADJ
using the ANUDEM v5.3 [40] algorithm implemented through the TOPO TO RASTER command
in ARCGIS v10.1. This algorithm generates a hydrologically correct raster surface from points, in
contrast to other interpolation technics such as Inverse Distance Weighting or Kriging. The streamlines
were generated applying the commonly used D8 algorithm [68], which involved the following steps:
filling sinks; generating flow direction; generating flow accumulation; generating stream network. The
river network was substantially improved with the help of a water/non-water classification obtained
from Landsat images to produce a “Ground Truth” Stream Network (GTSN). The point elevation set
was obtained by converting the DEM_ADJ raster to points after merging it with the 90 GCP dataset.
Optional parameters values adopted are gathered in Figure 3.
2.3.3. Quality Assessment of the Generated DEMs
Vertical Accuracy
The vertical accuracy assessment was estimated by comparison against the 10 GCPs dataset
through 4 criteria. Setting x as the datasets consisting of the elevations of the DEM pixels that contain
at least one GCP and y as the dataset consisting of the mean of the 10 GCP, we computed the following
statistics: the RMSE, the MEAN, the Standard Deviation (SD) of (x-y), and the ROUGHNESS, computed
as the mean of the standard deviation of a 3 ˆ 3 array of elevation spots.
Stream Network Assessment
For each generated DEM, the stream network was extracted following the D8 algorithm.
The networks were compared through three characteristics: CONNECTIVITY (Boolean), which
indicates whether the lake and igarapés are continuously connected; OUTLETS (Boolean), which
indicates whether the outlet is well positioned; and the MATCHING PERCENTAGE TO THE GTSN.
The latter is designed as the percentage of a stream network falling in a buffer of 200 m surrounding
the GTSN.
Flood Extent Assessment
In addition to HWFM and LWFM, we selected four Landsat images relatively free of clouds. Two
images are representative of the LW period, with water elevations at the RL1 gauge of 11.5 m and
13.3 m on their acquisition dates. The third image is representative of the flushing period, with a
corresponding water level of 18.4 m at the RL1 gauge. The fourth image is representative of the HW
period, with a corresponding water level of 22.5 m at the RL1 gauge. The classifications issued from
the images are denoted LFM11.5, LFM13.3, LFM18.4 and LFM22.5. Water areas were distinguished
from non-water areas computationally using the normalized difference ratio between the mid-infrared
band TM5 and the visible band TM2, used by Toivonen et al. [69] to map open water areas over a
2.2 million km2 portion of the western Amazon. It appeared that a constant threshold, as used in
Toivonen et al. [69], could not be applied to all Landsat images, so each image was associated with a
specific threshold. To reduce the number of misclassified pixels, we stated that a pixel classified as
“water” had to remain classified as “water” in the images representing higher water levels. Conversely,
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pixels classified as “non-water” had to remain classified “non-water” in the images representatives of
lower water levels.
The flooded area can be roughly represented by the intersection of the DEM with the interpolated
water surface, assuming a hypothesis of horizontality in the floodplain. Hence, it could be possible to
compute the extent of inundation from the DEM. Here, differences in level between upstream (SV2)
and downstream (SV1) are in the range (´0.4 m, 0.3 m) for 90% of the data. The water level difference
recorded at RL1 and RL2 is in the range of (´0.2 m, 0.5 m) for 90% of the data. Finally, we estimate that
the maximum slope that can appear in the floodplain is 2.5 cm/km. The water level at the different
stations distributed in the floodplain show that the horizontal assumption is reasonable.
The agreement between the flooding extent deduced from the DEM and imagery was calculated
using the following classical skills scores [36,70,71]: the threat score (TS) measures the model accuracy
with a perfect score of 100, whereas the bias index (BIAS) indicates the type of error (overestimation or
underestimation). These scores are determined using the following relations:
ˆ
TS “ 100 ˆ
a
a`b`c
ˆ
˙
a`b
BIAS “ 100 ˆ 1 ´
a`c
(1)
˙
(2)
where a represents the total area that is mapped as flooded both in the image and from the DEM, b is
the area flooded in the DEM but not in the image, and c is the area flooded in the image but not in
the DEM.
3. Results and Discussion
3.1. Biases Correction
Table 2 resumes all the results relative to the biases correction. The replacement of EGM96 by
EGM08 as the reference for the SRTMGL1 elevation resulted in lowering the terrain by a mean of
´0.3 m (SD = 0.1 m).
Table 2. DEM vertical accuracy assessment against 10 ground control point (GCP) dataset and river
network assessments.
DEM
Mean (m)
Standard Deviation (m)
Geoid bias
Interferometric bias
Total correction over the study area
Total correction restricted to the Terra Firme zone
´0.3
´2.0
5.9
7.4
0.1
4.1
6.9
7.3
Observations: Total correction includes: geoid, interferometric and vegetation correction.
The Mann-Whitney U test rejected the hypothesis of equal means between overlaps in bare soil
of altimetry and in situ elevations and SRTMGL1 at the 1% significance level. The interferometric
bias was estimated to be ´2.0 m (SD = 4.1 m). A vertical offset of this magnitude was applied to
increase the SRTMGL1 elevations. This bias is half the value encountered by Rudorff et al. [26] in the
Curuaí floodplain, located approximately 700 km downstream of the Janauacá floodplain. However,
as reported by Rodriguez et al. [31], the interferometric bias is expected to vary from place to place.
The total correction, including the ´2.0 m interferometric offset and the vegetation bias, has a
mean value of 5.9 m (SD = 6.9 m) and 7.4 m (SD = 7.3 m) over the whole study area and the terra firma
zone (ID51), respectively. As the SRTM elevation is located at approximately 40% of the distance from
the canopy top to the ground [34], we estimated that the mean canopy height was 12.3 m over the
terra firma zone. This value is consistent with the canopy heights found in studies of tree species in
central Amazonian floodplain forests [72,73] and is higher than the median vegetation offset of 1.4 m
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reported by Rudorff et al. [26] for the Curuaí floodplain, but this area includes a larger proportion of
savanna and secondary vegetation than the Janauacá floodplain. It is also much smaller than the value
of 22.4 m (SD = 11.8 m) estimated by Carabajal and Harding [34] or used in regional models (23 m and
17 m, suggested by Coe et al. [35] and Paiva et al. [36], respectively).
As mentioned earlier, Baugh et al. [25] proposed a procedure to remove vegetation based on the
FCHM. This method is attractive because it does not require any in situ data, but in the case of the
Janauacá floodplain, applying their methodology would have led to inconsistent results. As reported
in Figure 5, where we present the difference between the SRTMGL1 and the DEM_COR in terms of the
percentage of the FCHM, negative bias is found principally in floodable areas. In this area, SRTMGL1
elevations are too low, partly because of the interferometric bias compared with GPCs. Second, we
detected several incoherencies between the SRTM, WM and FCHM products that hinder obtaining
realistic results (Table 1). For example, vegetation height pixels overlapped by the herb WM classes
have a mean vegetation height of 6.5 m, whereas the herb class should be considered as bare ground.
Similarly, shrub lands present a mean vegetation height of 13.5 m, whereas Hess et al. [38] define them
with a height below 5 m. The horizontal and vertical accuracy of each product can explain these
incoherencies, but we essentially attribute these errors to the resampling process at the SRTMGL1
resolution. In this sense, the method proposed by Baugh et al. [25] is more likely to be suitable for
larger resolution studies.
Figure 5. (a) Percentage subtracted from map of Forest Canopy Height (FCHM) deduced from the
difference between the SRTMGL1 and DEM_COR, and associated (b) histogram restricted to the
watershed of DEM_COR (For interpretation of the references to color in this figure legend, the reader is
referred to the web version of this article).
In the study area, the mean percentage of vegetation height subtracted from FCHM was 13%, with
a large SD of 29%. The histogram repartition (Figure 5) of the percentage presents two modes, ´8% and
48%. The pixels from the (30%, 60%) class, which was the second most populated, are localized in the
upland of the catchment. For these pixels, the mean of the vegetation removal was near the intervals
of (50%, 60%) proposed by Baugh et al. [25]. On the banks, the mean percentage of vegetation removal
was 29%. However, DEM_COR banks are raised by 0.5 m on average, when all offsets (interferometric
and vegetation) and interpolations are considered. The corrections are more significant below SWBD,
where SRTMGL1 was raised by a mean value of 3.3 m (SD = 3.3 m).
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3.2. Accuracy Assessment
3.2.1. Vertical Accuracy Assessment
The vertical accuracy assessment against the 10 GCP dataset for the different generated DEMs
and the original SRTMGL1, as well as the roughness criteria, are gathered in Table 3. Clearly, DEMs
generated with in situ and altimetry data present a better vertical accuracy than the others. The mean
of the differences between the 10 GCP and DEM elevations decreased from ´1.2 m for SRTMGL1 to
0.1 m for DEM_COR. RMSE decreased from 4.7 m for SRTMGL1 to 1.7 m for DEM_COR. SD similarly
decreased from 4.5 m for SRTMGL1 to 1.7 m for DEM_COR. Conversely, the descriptor values for
DEM_COR_1 and DEM_COR_3, which does not include the 90 GCP dataset, remained similar to the
values obtained for SRTMGL1 as RMSE and SD remain greater than 4 m. As expected, the roughness
of the generated DEMs was significantly reduced, by approximately 50%, except for DEM_COR_1.
The roughness reduction is partly explained by the interpolation process. DEM_COR_2 presented the
lowest value as it was generated without the constraint of a stream network.
Table 3. DEM vertical accuracy assessment against 10 GCP dataset and river network assessments.
DEM
Mean
(m)
SD
(m)
SRTMGL1
DEM1
DEM_COR
DEM_COR_1
DEM_COR_2
DEM_COR_3
´0.4
1.3
0.1
0.9
0.4
1.9
4.7
4.7
1.7
4.3
1.6
4.6
RMSE Roughness Outlet
(m)
(m)
(Boolean)
4.8
4.9
1.7
4.4
1.6
4.9
1.5
1.5
0.9
1.1
0.7
0.8
0
0
1
1
0
0
Connectivity
(Boolean)
GTSN Matching
Index (%)
1
1
1
1
1
0
58
58
83
67
46
66
3.2.2. Stream Network Assessment
Table 3 presents the validation relative to the river network. Regarding horizontal agreement,
DEM_COR presents the best matching with the GTSN (83%), against percentages below 60% in the
case of the SRTMGL1 and all other DEMs generated without the GTSN. All the extracted networks,
except the one derived from DEM_COR_3, capture the connectivity between the different water bodies.
However, only the networks extracted from DEM_COR and DEM_COR_1 presented both connectivity
and right outlet position. In the case of DEM_COR_3, generated with the GTSN input but without
GCPs, two outlets were found. This result was partly due to the hydrological incoherence introduced by
the vegetation removal process, which resulted in turning 31% of the pixels into NODATA values 31%
of the pixels into NODATA values without any counterbalancing by GCPs. However, DEM_COR_2,
which was obtained without any drainage network as input to the ANUDEM algorithm, presented
the worst GTSN matching index and failed the connectivity and outlet tests. Thus, the use of accurate
streamlines as input data to generate the DEM through the ANUDEM algorithm is important.
3.2.3. Flood Extent Assessment
The values of the different skill scores (TS, BIAS) obtained comparing SRTMGL1 and the generated
DEMs with flood maps are reported in Table 4.
At low waters, compared with LWFM, all generated DEMs presented lower accuracy (TS) than
SRTMGL1, except for DEM_COR_3. The flooding extent deduced from the generated DEMs was
underestimated (positive BIAS), whereas SRTMGL1 presented a nearly 0% BIAS. However, the
relatively good agreement of SRTMGL1 with LWFM is very likely due to the interferometric bias,
which lowered the pixel elevations by 2 m. Indeed, DEM1 and DEM_COR_1 corrected only for
the interferometric bias presented a lower accuracy than SRTMGL1. Compared with LFM13.3, all
generated DEMs presented slightly better accuracy than SRTMGL1, with TS values ranging from 42
to 48 for generated DEMs against 40 for SRTMGL1. The inundated area overestimation in SRTMGL1
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(BIAS = ´127) was greatly reduced in the other DEMs (BIAS ě ´68). This trend was confirmed when
comparing the DEMs with the LFM11.5. At this very low water level, DEM_COR presented the best
accuracy, with a nearly 0 BIAS.
Table 4. Flooding extent: values of threat score (TS), bias index (BIAS) expressed in percent.
LWFM
HWFM
LFM11.5
LFM13.3
Landsat Images
LFM18.4
LFM22.5
13.5 m
22.1 m
20
´265
2
´27
37
3
26
´169
29
51
15
´84
40
´127
46
´63
48
´28
45
´68
42
9
44
´55
69
´28
72
´17
60
´43
75
´13
58
´35
60
´34
65
´19
69
´4
62
´17
71
2
59
´11
62
´12
40
´1
37
27
39
37
37
22
35
53
42
27
49
´29
53
´1
73
´24
53
´2
75
´18
75
´18
DEM
SRTMGL1
DEM1
DEM_COR
DEM_COR_1
DEM_COR_2
DEM_COR_3
TS
BIAS
TS
BIAS
TS
BIAS
TS
BIAS
TS
BIAS
TS
BIAS
During flushing, compared with LFM18.4, the best accuracies were obtained for SRTMGL1 and
for DEMs generated without vegetation correction (DEM1 and DEM_COR_1). For the remaining
DEMs, the TS values were approximately 15% lower. The flood extent was overestimated by all DEMs
(negative BIAS).
At high waters, compared with the HWFM, all generated DEMs presented better accuracy than
SRTMGL1. Negative BIAS, in all cases, stressed an overestimation of the flood extent. Compared with
the LFM22.5, the best accuracies were exhibited by SRTMGL1 and DEMs generated without vegetation
correction (DEM1 and DEM_COR_1). For the remaining DEMs, the TS values remained approximately
15% lower. The negative BIAS in all comparisons except for DEM_COR_1 showed an overestimation
of the flood extent.
The spatial distribution of pixels contributing to overestimation or underestimation of the flood
extent is shown in Figure 6 (DEM_COR was used for this purpose). At low water levels (Figure 6a,c,d),
mis-flooded pixels were mainly located in the northwestern part of the study area, where bathymetric
data indicated bottom elevation greater than 13.5 m. Some of these pixels very likely result from
the limitation of retrieving the flooding extent from a simple water surface interpolation as these
flooded regions remained isolated from the water body where the water level used to interpolate
the water surface is recorded (at RL1). This limitation may also explain some of the mis-flooded
pixels (Figure 6a,b) encountered far upstream along the igarapés, where the pixel inundated status is
more dependent on local runoff and seepage than on the floodplain inundation. Some of the pixels
contributing to underestimation of the flood extent when DEMs were compared with flood maps
derived from Landsat are likely due to mis-classification because of the difficulty of detecting water
under vegetation with the Landsat imager. Indeed, most overestimated pixels lie in the upstream part
of the catchment zone and around the Janauacá Lake (Figure 6e), where woody areas are encountered
according to WM. This finding is confirmed by the fact that their accuracy significantly increased
compared with HWFM instead of LFM22.5.
In addition, as found by Rudorff et al. [26] for the Curuaí floodplain, our results suggested
that LWFM very likely overestimates the flood extent in the Janauacá floodplain. The flooded area
mapped from Landsat at 13.3 m only represented 45% of the inundation area issued from JERS-1, a
difference that is hardly explained by Landsat mis-classification. Moreover, compared with LFM13.3,
all generated DEMs presented better accuracy with this product than with LWFM, except for SRTMGL1,
which strongly overestimated the extent of inundation likely due to an interferometric bias of ´2 m.
Flood extent overestimation mapping with LWFM level could also explain the relatively passable
results at LW level reported in other studies at larger spatial scales. For example, Wilson et al. [27]
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concluded that the accuracy (TS index) was less than 23 at the LW level, against 73 at the HW level.
The matching result of Yamazaki et al. [40] reached 40 at the LW level against 60 at the HW level. On a
global scale, Paiva et al. [36] found a model performance of 34 at the LW level, against 70 at the HW
level. As mentioned by Hess et al. [38], the inundation mapping accuracies for SAR images acquired at
low stages decrease as pixels are subjected to a signal mixture, especially pixels overlapping flooded
vegetation and open-water surfaces.
Figure 6. Comparison of inundation extent between DEM_COR and classification from JERS-1 and
Landsat (For interpretation of the references to color in this figure legend, the reader is referred to the
web version of this article). (a) DEM_COR vs. LWFM, (b) DEM_COR vs. HWFM (c) DEM_COR vs.
LFM 11.5, (d) DEM_COR vs. LFM 13.3, (e) DEM_COR vs. LFM 18.4, (f) DEM_COR vs. LFM 22.4.
Figure 7 highlights the tendencies of each flooding extent index along the comparison of each
DEM against different flooding extent maps. From a general perspective, the dispersion among
products for each was considered with increasing waters. This result suggests that at the HW level, the
products are analogous in term of flood extent. More specifically, accuracy increases with increasing
water by a factor of 3 on average. The BIAS value tends to 0. Thus, at the HW level, all the DEMs
tend to present the same equal proportions of over- and under-estimated pixels during the validation.
Finally, at the LW level, disparities between DEMs are important, highlighting that flooding extent is
sensitive to each modification of DEM, whereas at the HW level, all DEMs present similar indexes.
At LW, the proposed methodology clearly enhanced the DEM product. However, all DEMs present
similar skill scores at the HW level. Indeed, in the unforested regions, the methodology reduced the
interferometric bias (´2 m), which is negligible compared to the tide (near 12.0 m). Over the forested
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zones located in the wetlands, interferometric and vegetation corrections compensate each other, as
confirmed in Figure 5 (8% of the vegetation height is removed, corresponding to 0.5 m).
Figure 7. Graph of the indexes of the flooding extent validation. (a) TS along different validations,
(b) BIAS along different validatios.
3.3. Implications for Morphological and Hydrological Characteristics
Table 5 summarizes the morphological and hydrological characteristics of the watershed
delineated from SRTMGL1 and the generated DEMs in terms of total area, low and high water
flooded areas, slopes, longest flow paths and concentration times. The catchment areas extracted from
the different generated DEMs varied by 14% among the different products. The size of the longest
flow path varies by +29%, from 68 km to 96 km, among the different DEMs. As expected, the slope
is greatly reduced, ranging from 31 cm/km to 59 cm/km for all generated DEMs against 88 cm/km
for SRTMGL1.
Table 5. Morphological characteristics of numerically delineated catchments from the different DEMs.
Drainage
Network
Watershed
(km2 )
LW Flooded
Area (km2 )
HW Flooded
Area (km2 )
Longest Flow
Path (km)
SRTMGL1
DEM1
DEM_COR
DEM_COR_1
DEM_COR_2
DEM_COR_3
795
805
786
737
854
788
88
31
23
46
10
44
397
309
391
304
406
384
68
69
96
94
83
81
Slope
Concentration
(cm/km)
Time (h)
88
87
31
59
36
35
26
26
50
38
42
41
We estimated the flooded area of each DEM by intersecting the interpolated water surfaces
with the DEM floodplain (Table 5). At the 11.5 m water level, the DEM_COR flooding extent
was estimated to 23 km2 , i.e., 3% of the watershed. At the highest water level recorded (24.3 m
in 2012), the DEM_COR flooding extent was estimated to be 391 km2 , i.e., 50% of the associated
watershed. DEM1 and DEM_COR_1 presented the lowest flooding extent as they have been raised by
the positive interferometric offset, but vegetation offset was not removed from these DEMs. Apart from
DEM_COR_1 and DEM1 , the dispersion between flood extents derived from the different DEMs was
significantly reduced for water levels above 15.5 m. At a water level of 11.5 m, the dispersion around
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the mean value of the DEMs represents 83% of the mean flood extent. At 15.5 m, this dispersion around
the mean value among DEMs is already reduced to 7% and is less than 4% for all water levels above.
These variations in morphological characteristics may influence some classical parameters used
to interpret the hydrological watershed response. In particular, the Kirpich concentration time, used
in the MGB-IPH model [74], which has been intensively applied in the Amazon basin, varies by 95%
among all the DEMs. It ranged from 26 h to 50 h for SRTMGL1 and DEM_COR, respectively. In local
floodplain studies, where data are sparse, a classical approach is to approximate the local runoff by
the linear formula Q = c ˆ I ˆ At f , where Q is the peak discharge (m3 /yr), I is the rainfall intensity
(m3 /m2 ), At f is the emerged area within the catchment (m2 ), and c is the runoff coefficient. The value
of c is fixed at 0.58, regarding a wide basin average and local runoff coefficient from other lakes in the
lowland basin [26,75,76]. The difference in terms of watershed and flooded area between SRTMGL1
and DEM_COR, for example, would lead to a reduction of 8% in terms of runoff at low water level and
to an increase lower than 1% at high water (Figure 8b) if SRTMGL1 was used instead of DEM_COR.
Figure 8. Comparison between different DEMs in terms of water surface and local inflow runoff.
(a) Hypsometric curves of the Janauaca floodplain, (b) Dry areas in function of water level.
Along the banks, the bias correction and interpolation led to raising the bank pixels by 0.5 m
on average when comparing DEM_COR against SRTMGL1. Such a difference delays the diffusive
overbank flow by a few days, typically less than 15 days considering an increase of water level of
4 cm/day in the Solimões River. It will also slightly reduce overbank flow.
3.4. Usefulness of Bathymetric Data in the Correction Process
Collecting in situ bathymetric data on the whole Amazon basin is not conceivable. The comparison
between the different generated DEMs helps better highlight the improvement brought by the
bathymetric in situ data. It is interesting to note that apart from LW level, the parameters derived
from DEM_COR_3 and DEM_COR are relatively similar. In particular, for these two DEMs, watershed
and flooding extent are comparable, as well as skill scores related to the flooding extent obtained at
medium and high water levels. DEM_COR_1 also presented better skill scores and a more realistic
watershed extent than DEM_COR_2, which included bathymetric data but not GTSN in the ANUDEM
interpolation step. Thus, as far as watershed hydrological characteristics and flooding extent are
concerned, the use of in situ bathymetric data is not necessarily required as long as a realistic drainage
network is furnished. Indeed, altimetry data could be used to compute the interferometric bias
with reasonable confidence if the study window is large enough to overlap a reasonable number of
ICESat/GLAS points. Improvement of the flooding extent at the LW stage might also be induced,
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selecting the minimum values of altimetry data for the pixels overlapped by SWBD, following a similar
method as proposed by Pfeffer et al. [77].
4. Conclusions
Hydrodynamic models are attractive tools for quantifying river-floodplain water exchanges and
for studying water circulation patterns in the floodplain, but they require relatively high quality
topography to produce realistic results. To date, the best free readily available topographic information
for the lower Amazon basin is the most recent SRTMGL1 release, at 1 arc-second of resolution. However,
this dataset still presents inconsistent elevations.
We proposed a method to remove some of the errors related to interferometric and vegetation bias,
mobilizing in situ bathymetric data, altimetry data and flooding extent mapped from remote sensing.
The interferometric bias was estimated to be ´2.0 m. The mean total offset correction over the
study area was 5.9 m, and 7.4 m for pixels located in the upland region. As the SRTM elevation is
located approximately 40% of the distance from the canopy top to the ground, the latter value led to a
mean canopy height of 12.3 m, which was reasonable considering the proportion of secondary forest
in the region.
In a second step, unbiased elevations were interpolated using the ANUDEM v5.3 algorithm.
Several DEMs were generated to control for the respective influence of using or not using a GCP
dataset or a ground-truth drainage network in the interpolation process. The vegetation correction and
the interpolation process made it possible to reduce the DEM roughness by almost 50%. As expected,
using GCPs clearly improved the vertical accuracy: the RMSE value decreased from 4.7 m to less
than 2 m for all DEMs that included the GCP dataset. The use of GCPs also improved the flooding
extent predicted by the DEM, independently of using or not using a drainage network at low water
levels. The correction method improved the agreement between the flooding extent derived from the
DEMs and from remote sensed products at low and high water levels (+10% and +27%, respectively),
whereas accuracy at a medium water level was difficult to evaluate using the Landsat product.
At medium and high water levels, the use of a ground truth drainage network as input to the
interpolation algorithm permitted the algorithm to achieve relatively reasonable results regarding
flooding extent and watershed hydrological characteristics, even with DEMs that did not include GCPs.
This result is promising from the perspective of the application of the method, at least for hydrological
studies at larger scale as radar altimetry can likely furnish a sufficient GCP dataset to estimate the
interferometric bias.
Finally, we investigated the influence of the different DEMs on the numerical retrieval of
morphological watershed characteristics and consequently on some hydrological properties. Using the
D8 algorithm, the best generated DEM (identified as DEM_COR in this study) according to our criteria
led to a 786 km2 -wide watershed area, whereas we obtained 795 km2 with SRTMGL1. The minimum
flooding extent was 23 km2 , representing 3% of the generated DEM watershed, instead of 88 km2
(i.e., 10% of the watershed) for SRTMGL1. The maximum flooding extent derived from the best
generated DEM was 391 km2 , whereas we obtained 397 km2 for SRTMGL1, thus neglecting error
during high waters. In both cases, the result represented 50% of the Janauacá catchment extent.
The longest flow path deduced from the generated DEM was 30% greater than when deduced from
SRTMGL1. These differences produced an almost double Kirpich concentration time if computed
from SRTMGL1 (26 h) or the generated DEM (50 h). In terms of local runoff and assuming a linear
runoff formula, the difference in terms of the watershed and flooded area between SRTMGL1 and
the generated DEM would lead to an increase of 10% in terms of runoff at low water level and to a
reduction of less than 1% at high water level if using the corrected DEM.
Acknowledgments: This research was conducted in the framework of the CNPq (Conselho Nacional de
Desenvolvimento Científico e Tecnológico, Brasil) and IRD (Institut de Recherche pour le Developpement) n˝
490634/2013-3, and LMI OCE (Laboratoire Mixte International Observatoire des Changements Environementaux).
It was supported by three research programs: HIDRAS (CNPq), Dinâmica Fluvial do Sistema Solimões/Amazonas
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(CPRM), ANZIM (CNES/TOSCA). The first author is grateful to CAPES (Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior) in Brazil for financial support.
Author Contributions: Marie-Paule Bonnet and Stephane Calmant conceived and designed the experiments;
Marie-Paule Bonnet, Stephane Calmant, Joecila Santos Da Silva and DanielMoreira performed the in situ
experiments; Marie-Paule Bonnet and Sebastien Pinel analyzed the data. Joecila Santos Da Silva, Stephane
Calmant, Frederique Seyler, Daniel Moreira and Frederic Satgé contributed in resolving problems during the data
processing; Marie-Paule Bonnet and Sebastien Pinel wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
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© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
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